PyTorch model training and layer saturation monitor

Official implementation of the NeurIPS 25 Paper: "Diffusion Generative Modeling on Lie Group Representations"

Computational neural network model incorporating neuromorphological alterations present in animal models of Down Syndrome. Main publication: https://doi.org/10.1371/journal.pcbi.1012259

Information Theoretic MPC with Neural Dynamics Modeling

Biological and medical applications of the Symmetrical Convergence (SymC) framework. Exploring critical damping in cellular, neurological, and immune systems for stability and adaptive control.

A Brian2-based spiking neural network model for perceptual decision-making, featuring selective inhibition control and top-down modulation. The GUI supports real-time visualization and comprehensive analyses, including single-trial and multiple-trial performance metrics such as psychometric curves, reaction times (RT), and energy landscapes

Code and data of the paper Clusella et al. "Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks" (2023) https://doi.org/10.1371/journal.pcbi.1010781

Neurophysical core — private. This repository contains a public showcase: a brief description of capabilities, an example (raw) dataset, and a demo video of the neurophysical core in action.

An Engine for Dynamic Enhancement and Noise Overcoming in Spatiotemporal Multimodal Neural Observations via High-density Microelectrode Arrays

Static Belief Propagation - Message Passing algorithms for the computation of the magnetization and correlation, at any temperature, in both the spin-glass and ferromagnetic Ising model

This repository contains the implementation of a wide variety of Neuroscience Projects in different applications of computational neuroscience, behavioral analysis, task design with Psychopy. These projects include university projects and projects implemented due to interest in Neuroscience.

Semester project to compute the Lyapunov exponents associated to neuronal dynamics of C. Elegans.

A framework for constructing digital twins of brain dynamics. Reconstruct functional networks from signals and run simulations using coupled oscillator models.

Modified Gillespie Algorithm for changing input regimes.

Fourier analysis of neuron activity data. Identify neuron associated with periodic perturbation.